Modern statistics has become almost synonymous with machine learning - a collection of techniques that utilize today's incredible computing power. Jared Lander walks you through the available methods for implementing machine learning algorithms in R and explores underlying theories such as the elastic net and boosted trees.

• Building the design matrix
• Penalized regression with the lasso and ridge methods
• Fitting models with glmnet
• Interactive visualization of the coefficient path
• Use cross-validation to choose the optimal lambda
• Visualize coefficients with coefplot
• Perform binary classification with a single tree with xgboost
• Train a boosted tree
• Tune xgboost hyperparameters
• Use validation data to understand performance
• Visualize variable importance
• Train a boosted random forest with xgboost

The field of Machine Learning has gone through some phenomenal changes over the last decade. In the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. In this talk, I will be covering the need and importance of human interpretable machine learning approaches, look at effective strategies for model interpretation and several hands-on examples. Detailed coverage of open-source frameworks for machine learning model interpretation will also be one of the major focus areas. Examples will be showcased in Python.

Transfer learning is a machine learning \ deep learning technique where knowledge gained during training in one set of machine learning problem can be used to train other similar types of problems. This is an extremely useful approach to leveraging pre-trained models to solve real-world problems having constraints and limitations of less data availability.

This talk will cover essentials around deep learning and transfer learning concepts. The various methodologies of transfer learning. We will then look at diverse ways of how transfer learning can be applied in the real-world on complex problems around the following areas.

• Computer Vision
• Natural Language Processing
• Audio Categorization

We will briefly look at a multitude of real-world case studies and problems around the preceding areas like text classification, image classification, image captioning, style transfer and audio classification.

FastText has been open-sourced by Facebook in 2016 and with its release, it became the fastest and most cutting edge library for text classification and word representation. It includes the implementation of two extremely important methodologies in NLP i.e Continuous Bag of Words and Skip-gram model. FastText performs exceptionally well with supervised as well as unsupervised learning.